Introduction

Alzheimers Disease (AD) is a progressive disease that destroys memory and other important mental functions. It is the 6th leading cause of death in the US, and it does not have a cure. The latter fact strongly affects a family of a newly diagnosed with AD patient. Exploring the landscape of ongoing clinical trials, targeting AD, is the next step family takes to assess possible treatment options. Clinicaltrials.gov is a database of clinical studies conducted around the world and is a great place to start exploring experimental treatment possibilities. It has user friendly interface and provides extensive amount of clinical trials information. However, it could be overwhelming for someone without special training to dive through tabulated extensive information available at clinicaltrials.gov. A web-based visualization tool, which summaries Alzheimer`s Disease clinal trial information while sourcing data from clinicaltrials.gov, could be a useful tool for those families, impacted by the illness and seeking a quick visual reference guide on Alzheimers Disease clinical trials past and present research.

Data and methods

File names [Imaani]

[### Loading data]

[### Reorganizing into usable form]

[### Any general-preprocessing steps?]

Summary statistics [Faith/Volha?]

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data = 
  read_csv("Data/SearchResultsTable.csv") %>%
  clean_names() 
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[Mean, sample size etc.]

Exploratory analysis [Volha will combine three figures]

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data_for_plot = 
  data %>% 
  select(rank, nct_number, phases, study_type, enrollment, nct_number, age) %>% 
  mutate(age_start   = str_sub(age,  1,  3) ) %>%
  mutate(age_start = replace(age_start, age_start == "Chi", 0)) %>%
  mutate(age_start = replace(age_start, age_start == "up ", 0)) %>%
  separate(age_start, c("age_start", "remove"), " ") %>%
  mutate(age_start = as.numeric(age_start)) %>%
  mutate(n = as.numeric(enrollment)) %>%
    mutate(age_end = str_sub(age, 13, 14)) %>%
  # replace "...Years and older   (Adult, Senior)" with age_end==100
  mutate(age_end = replace(age_end, age_end == " o", 100)) %>%
  #replace "Child, Adult, Senior" with age_end == 100
  mutate(age_end = replace(age_end, age_end == ", ", 100)) %>%
  #replace "up to 100 Years   (Child, Adult, Senior)" with age_end==100
  mutate(age_end = replace(age_end, age_end == "ar", 100)) %>%
  # replace "...Years and older   (Child, Adult, Senior)" for those with 
  # starting one digit age with age_end == 100
  mutate(age_end = replace(age_end, age_end == "ol", 100)) %>%
  # replace "up to ..Years   (Child, Adult, Senior)" where ... is 2 digit
  # with age_end == 100
  mutate(age_end1 = str_sub(age, 7, 8)) %>%
  mutate(age_end = replace(age_end, age_end == "rs", age_end1[age_end == "rs"])) %>%
  # convert age_end to numeric
  mutate(age_end = as.numeric(age_end)) %>%
  select(-c(remove, age_end1)) %>%
  # reorder 
  mutate(nct_number = forcats::fct_reorder(nct_number, age_start))


# Prepare data for line plot - convert into long format and group by nct_number:   

data_for_line_plot = 
  data_for_plot %>%
  gather(key = "eligible_age", value = "years", age_start, age_end) %>%
  group_by(nct_number) %>%
  arrange(years)
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Formal analysis [Faith?]

Results [Volha and all]

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Figure 1: Interactive plot with lines for eligible age for different clinical trail phases:

p = data_for_line_plot %>%
  na.omit() %>% 
  #filter(study_type == "Interventional") %>%
  #filter(study_type == "Observational") %>%
  ggplot(aes(x = years, y = n, color = phases, group = nct_number)) +
  geom_path() +
  facet_wrap(~study_type) +
  labs(x = "eligible age",
       y = "enrollment",
       caption = "Figure 1") +
  theme_bw()

ggplotly(p)
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Figure 1: Gantt chart

Figure 2: Interactive plot with lines for eligible age for different clinical trail phases

Figure 3:

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Shiny dashboard simplifies Alzheimers Disease landscape research for an inexperienced user and as such provides benefit for a family of a newly diagnosed Alzheimers Disease patient.

Discussion